Maximizing children’s physical activity: an evaluability assessment to plan a community-based, multi-strategy approach in an ethno-racially and socio-economically diverse city
Bibliographic record
Abstract
An evaluability assessment was conducted to plan a community-based, multi-strategy approach to physical activity promotion (MSAPAP) to maximize young children's physical activity in an ethno-racially and socio-economically diverse city. This assessment involved consultation with various stakeholders to develop a program logic model to diagrammatically describe the MSAPAP. First, published literature regarding physical activity was reviewed to describe interventions designed to increase children's physical activity and to identify factors that contributed to program effectiveness. Secondly, key informants from mainstream service organizations and smaller community-based agencies were interviewed to determine their views on how to increase physical activity among children and families. A workgroup developed a draft logic model based on the results of the literature review and community needs assessment results. Thirdly, stakeholders were consulted about the draft model. This consisted of 12 focus groups with members of school boards (two sessions), members of community organizations (three sessions), lay home visitors who provide support to mothers of young children in ethno-racially diverse communities (one session), and parents from six cultural groups (six sessions). The logic model was revised based on the findings from this consultation. The final logic model shows children aged 3-8 years as the main target group, and parents and various community members who influence children as intermediate target groups. The MSAPAP is depicted as six strategies, which are clusters of program activities that are conceptually similar: community engagement, community assessment, accessibility, promotion, education and skill development, and inclusive programming. The logic model shows the 'cause and effect' relationships among program activities, shorter-term outcome objectives (e.g. to reduce user fees for physical activity programs) and longer-term outcome objectives (e.g. to increase the proportion of children who are physically active). The extensive community involvement in planning the MSAPAP facilitated a subsequent plan to develop, implement and evaluate selected program activities in the MSAPAP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".